AAAI Publications, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence

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DoSTra: Discovering Common Behaviors of Objects Using the Duration of Staying on Each Location of Trajectories
Limin Guo, Guangyan Huang, Xu Gao, Jing He, Bin Wu, Haoming Guo

Last modified: 2015-04-01

Abstract


Since semantic trajectories can discover more semantic meanings of a user’s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home Restaurant Company Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

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